RL often provides a comprehensive iterative computational framework that implies modular goal-directed interactions of an agent with its environment. More recently, Novati et al. [1]} demonstrated the power of RL in discovery of turbulence closure models in large-eddy simulation of turbulent flows. The RL framework being introduced in the turbulence modeling context is quite comprehensive, and might apply equally well to other coarse-grained reduced order modeling approaches. In relevant works [2]}, [3]}, the feasibility of using RL to learn optimal ROM closures has been discussed. We also refer to a recent review [4]} for the theory and application of RL approaches in fluid mechanics.
